A Territory Defining Multiobjective Evolutionary Algorithms and Preference Incorporation

Karahan, Ibrahim; Köksalan, Murat

doi:10.1109/tevc.2009.2033586

Cited by 76 publications

(24 citation statements)

References 21 publications

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“…Specifically in [51], DMO is implemented in NSGA-II and the crowding distance is regarded as the diversity promotion mechanism. [72]. TDEA is a steady-state algorithm based on the concept of territory.…”

Section: B Real Examplesmentioning

confidence: 99%

Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization

Yang

Liu

2014

IEEE Trans. Cybern.

135

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Diversity assessment of Pareto front approximations is an important issue in the stochastic multiobjective optimization community. Most of the diversity indicators in the literature were designed to work for any number of objectives of Pareto front approximations in principle, but in practice many of these indicators are infeasible or not workable when the number of objectives is large. In this paper, we propose a diversity comparison indicator (DCI) to assess the diversity of Pareto front approximations in many-objective optimization. DCI evaluates relative quality of different Pareto front approximations rather than provides an absolute measure of distribution for a single approximation. In DCI, all the concerned approximations are put into a grid environment so that there are some hyperboxes containing one or more solutions. The proposed indicator only considers the contribution of different approximations to nonempty hyperboxes. Therefore, the computational cost does not increase exponentially with the number of objectives. In fact, the implementation of DCI is of quadratic time complexity, which is fully independent of the number of divisions used in grid. Systematic experiments are conducted using three groups of artificial Pareto front approximations and seven groups of real Pareto front approximations with different numbers of objectives to verify the effectiveness of DCI. Moreover, a comparison with two diversity indicators used widely in many-objective optimization is made analytically and empirically. Finally, a parametric investigation reveals interesting insights of the division number in grid and also offers some suggested settings to the users with different preferences.

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Section: B Real Examplesmentioning

confidence: 99%

Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization

Yang

Liu

2014

IEEE Trans. Cybern.

135

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“…This system extracts features from images by wavelet transform, and the authors claim to provide a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. In another investigation by Karahan and Koksalan [21], a steady-state elitist evolutionary algorithm has been developed to approximate Pareto-optimal frontiers of multiobjective decision making problems. The algorithm defines a territory around each individual to prevent crowding in any region.…”

Section: Applying Explicit Preference Informationmentioning

confidence: 99%

Metaheuristic Design Pattern

Aljawawdeh

Simons

Odeh

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In interactive metaheuristic search, the human helps to steer the trajectory of the search by providing qualitative evaluation to assist in the selection of solution individuals. It can be challenging to design mechanisms to exploit human qualitative evaluation taking into account human trade-off judgments among competing criteria and implicit knowledge and experience. This paper addresses this challenge by proposing the Preference metaheuristic design pattern involving six abstractions across two dimensions: explicit and implicit knowledge and a priori, interactive and a posteriori timings.

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“…-Incorporation with decision makers Usually, decision makers do not need all the optimal solutions of MaOPs (Cvetkovic and Parmee 2002). They can input their interested regions or preferences to obtain parts of the nondominated solution set (Sindhya et al 2011;Ben Said et al 2010;Koksalan and Karahan 2010;Wang et al 2013a;Kim et al 2012;Karahan and Koksalan 2010;Giagkiozis and Fleming 2014). Additionally, decision makers have different targets for different objectives and multi-target search was employed (Wang et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

Objective reduction based on nonlinear correlation information entropy

Wang

Yao

2015

Soft Comput

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It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multiobjective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). Objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs.

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A Territory Defining Multiobjective Evolutionary Algorithms and Preference Incorporation

Cited by 76 publications

References 21 publications

Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization

Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization

Metaheuristic Design Pattern

Objective reduction based on nonlinear correlation information entropy

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